Event prediction through monitoring a mobile device

ABSTRACT

A method includes monitoring a geospatial location of a user of a mobile device having a processor communicatively coupled to a memory through the mobile device, date stamping and time stamping the geospatial location of the user through the mobile device, and monitoring, through a server having another processor communicatively coupled to another memory and/or the mobile device, an interaction of the user with the mobile device and/or a device communicatively coupled to the server based on the geospatial location of the user. The method also includes predicting, through the server and/or the mobile device, an event related to the mobile device and/or the device based on the monitoring of the interaction of the user therewith, and enabling, through the server and/or the mobile device, automatic performance of an action on the mobile device and/or the device on behalf of the user in accordance with the prediction of the event.

FIELD OF TECHNOLOGY

This disclosure relates generally to mobile devices and, moreparticularly, to a method, an apparatus and/or a system of eventprediction through monitoring a mobile device.

BACKGROUND

An individual possessing a mobile device (e.g., a mobile phone) mayexhibit an identifiable pattern of behavior. For example, the individualmay access the Internet through the mobile device. In addition, theindividual may maintain a record of activities through the mobiledevice. Further, the individual may frequently access external devicessuch as a computer, a projector, a media player etc. and/or may contactusers of other mobile devices. The individual may expend considerableamounts of physical energy and/or mental energy in controlling factorsnecessary to successfully complete a task (e.g., adjusting a settingsuch as temperature, communicating status information to another mobiledevice) on a recurring (e.g., daily, weekly, monthly) basis. Theaforementioned factors may require manual control on behalf of the user,which is subject to detrimental factors such as fatigue, stress and/orlack of time, leading to a less-than-optimal adjustment thereof.

SUMMARY

A method, an apparatus and/or system of event prediction throughmonitoring a mobile device are disclosed.

In one aspect, a method includes monitoring a geospatial location of auser of a mobile device having a processor communicatively coupled to amemory through the mobile device, date stamping and time stamping thegeospatial location of the user through the mobile device, andmonitoring, through a server having another processor communicativelycoupled to another memory and/or the mobile device, an interaction ofthe user with the mobile device and/or a device communicatively coupledto the server based on the geospatial location of the user. The methodalso includes predicting, through the server and/or the mobile device,an event related to the mobile device and/or the device based on themonitoring of the interaction of the user therewith, and enabling,through the server and/or the mobile device, automatic performance of anaction on the mobile device and/or the device on behalf of the user inaccordance with the prediction of the event. The event is in a temporalfuture relative to the prediction.

In another aspect, a system includes a computer network, a mobile devicecommunicatively coupled to the computer network, and another devicecommunicatively coupled to the computer network. The mobile deviceincludes a processor communicatively coupled to a memory. The processorof the mobile device is configured to execute instructions for receivinga geospatial location of a user of the mobile device, date stamping andtime stamping the geospatial location of the user, monitoring aninteraction of the user with the mobile device and/or the another devicecommunicatively coupled to the computer network based on the geospatiallocation of the user, predicting an event related to the mobile deviceand/or the another device based on the monitoring of the interaction ofthe user therewith, and enabling automatic performance of an action onthe mobile device and/or the another device on behalf of the user inaccordance with the prediction of the event. The event is in a temporalfuture relative to the prediction.

In yet another aspect, a system includes a computer network, a mobiledevice communicatively coupled to the computer network, another devicecommunicatively coupled to the computer network, and a servercommunicatively coupled to the mobile device and the another devicethrough the computer network. The server includes a processorcommunicatively coupled to a memory, with the processor being configuredto execute instructions for receiving a date-stamped and time-stampedgeospatial location of a user of the mobile device, monitoring aninteraction of the user with the mobile device and/or the another devicecommunicatively coupled to the computer network based on the geospatiallocation of the user, predicting an event related to the mobile deviceand/or the another device based on the monitoring of the interaction ofthe user therewith, and enabling automatic performance of an action onthe mobile device and/or the another device on behalf of the user inaccordance with the prediction of the event. The event is in a temporalfuture relative to the prediction.

The methods and systems disclosed herein may be implemented in any meansfor achieving various aspects, and may be executed in a form of amachine-readable medium embodying a set of instructions that, whenexecuted by a machine, cause the machine to perform any of theoperations disclosed herein.

Other features will be apparent from the accompanying drawings and fromthe detailed description that follows.

DESCRIPTION OF THE DIAGRAMS

Example embodiments are illustrated by way of example and not limitationin the figures of the accompanying drawings, in which like referencesindicate similar elements and in which:

FIG. 1 is a schematic view of a predictive system, according to one ormore embodiments.

FIG. 2 is a schematic view of an interaction of a user of a mobiledevice in the predictive system of FIG. 1 with the mobile device.

FIG. 3 is a schematic view of an interaction of the user of the mobiledevice with other devices at a place of habitation of the predictivesystem of FIG. 1.

FIG. 4 is a schematic view of the mobile device and the other devices ofthe predictive system of FIG. 1 communicating information to a server.

FIG. 5 is a schematic view of an event being communicated to the otherdevices as a result of analysis through an algorithm module of theserver in the predictive system of FIG. 1.

FIG. 6 is a schematic view of a number of events predicted at respectivetemporal windows of time through the predictive system of FIG. 1.

FIG. 7 is a schematic view of prediction through the mobile deviceinstead of the server in the predictive system of FIG. 1.

FIG. 8 is an illustrative view of an example scenario of predictionthrough the mobile device of FIG. 1.

FIG. 9 is a process flow diagram detailing the operations involved in amethod of predicting an event associated with the mobile device and/orthe devices of the predictive system of FIG. 1, according to one or moreembodiments.

Other features of the present embodiments will be apparent from theaccompanying drawings and from the disclosure of the variousembodiments.

DETAILED DESCRIPTION

A method, an apparatus and/or a system of event prediction throughmonitoring a mobile device are disclosed. In the following description,for the purpose of explanation, numerous specific details are set forthin order to provide a thorough understanding of the various embodiments.It will be evident, however, to one skilled in the art, that the variousembodiments may be practiced without these specific details.

FIG. 1 shows a predictive system 100, according to one or moreembodiments. In one or more embodiments, predictive system 100 mayinclude a mobile device 102 (e.g., a mobile phone, a Personal DigitalAssistant (PDA), a tablet computing device, a watch, a GlobalPositioning System (GPS) device) configured to communicate with a server104 through a computer network (e.g., network 106). In one or moreembodiments, server 104 may also be communicatively coupled to one ormore devices 110 (e.g., lighting device 110A, temperature control device110B, projector device 110C, computing device 110D, mobile device 110E)at a place of habitation 108 (e.g., place of residence, office,gymnasium) through network 106. While an example set of five devices areshown at place of habitation 108, it is obvious that place of habitation108 may include merely one device, less than five devices or more thanfive devices. In one or more embodiments, place of habitation 108 may beassociated with a user 120 of mobile device 102 and/or a user of mobiledevice 110E (to be discussed later).

In one or more embodiments, predictive system 100 may enable mobiledevice 102 to control, access and/or predictively adjust devices 110. Inone or more embodiments, server 104 may be a data processing devicehaving a processor 172 (e.g., a Central Processing Unit (CPU))communicatively coupled to a memory 174 (e.g., a Random Access Memory(RAM), a Read-Only Memory (ROM)) executing a set of instructions (e.g.,a computer program) therefor. In one or more embodiments, network 106may be the Internet or a Wide Area Network (WAN), a Storage Area Network(SAN), a Local Area Network (LAN), a Wi-Fi™ based network, a Bluetooth®based network etc. Other examples of place of habitation 108 may be aconference room or a car. Any enclosure providing user 120 (e.g., asshown in FIG. 2) and/or the another user (e.g., a user of mobile device110E) space to perform tasks on devices 110 may be construed as place ofhabitation 108.

In one or more embodiments, mobile device 102 may periodicallycommunicate a geospatial location thereof (to be discussed with regardto FIG. 2) and/or an interaction of user 120 therewith to server 104. Inone or more embodiments, devices 110 may also be configured toperiodically communicate interaction(s) of user 120 therewith to server104.

FIG. 2 shows interaction 202 of user 120 with mobile device 102,according to one or more embodiments. Interaction 202 may be anoperation performed by user 120 on mobile device 102. Examples ofinteraction 202 of user 120 with mobile device 102, as shown in FIG. 2,may include an electronic communication pattern 202A, a mobileapplication usage pattern 202B and a geospatial motion characteristic202C (e.g., physical location coordinates of user 120). Other examplesare within the scope of the exemplary embodiments discussed herein. Asshown in FIG. 2, mobile device 102 may also include a processor 210communicatively coupled to a memory 212 (e.g., a flash memory, a DynamicRAM (DRAM), a Static RAM (SRAM)) thereof. Mobile device 102 may executeapplications (e.g., through processor 210) to generate a geospatiallocation thereof and/or a date/time. The aforementioned execution maygenerate geospatial location 204 of mobile device 102, clock time 206and calendar date 208. Geospatial location 204 may obviously be afunction of clock time 206 and calendar date 208, as geospatial location204 may vary with clock time 206 and calendar date 208. Theaforementioned results of execution may be communicated from mobiledevice 102 to server 104.

Examples of user 120 include but are not limited to an event organizer,a meeting planner, and a building keeper. User 120 may interact withmobile device 102 to make conference calls therethrough, create eventsutilizing a calendar thereon, enter a destination thereon etc.Geospatial location 204 may be the spatial coordinates associated withplace of habitation 108 (and/or location of user 120), clock time 206may indicate a time at which user 120/mobile device 102 is at geospatiallocation 204, and calendar date 208 may indicate a date at which user120/mobile device 102 is at geospatial location 204. Thus, interaction202 may be associated with geospatial location 204, clock time 206 andcalendar date 208, thereby being time-stamped, date-stamped andlocation-stamped therewith.

Memory 212 of mobile device 102 may store information such asinteraction 202 and geospatial location 204 therein. As shown in FIG. 2,server 104 may monitor interaction 202 of user 120 with mobile device102. In an example embodiment, server 104 may periodically monitorinteraction 202 as a function of geospatial location 204, clock time 206and calendar date 208. Mobile device 102 may periodically communicatethe aforementioned geospatial location 204 and interaction 202 as thefunction of geospatial location 204, clock time 206 and calendar date208 to server 104 through network 106 based on instruction executionthrough processor 210. In one or more embodiments, server 104 (e.g., byexecuting a set of instructions through processor 172; the set ofinstructions may be stored in memory 174) may analyze data transmittedthereto to determine interaction 202 to be electronic communicationpattern 202A, mobile application usage pattern 202B and/or geo spatialmotion characteristic 202C.

FIG. 3 shows interaction 302 of user 120 with devices 110 in place ofhabitation 108, according to one or more embodiments. As seen in FIG. 3,each of devices 110 may also include a processor 372 communicativelycoupled to a memory 374. Again, in one or more embodiments, interaction302 may be an action that user 120 performs on one or more of devices110. Example actions with additional reference to FIG. 1 may includeturning on a light (related to lighting device 110A), adjustingtemperature (related to temperature control device 110B) and setting upprojector device 110C in place of habitation 108. Application(s)executing on each of devices 110 may generate a clock time 306 and acalendar date 308, analogous to mobile device 102. Clock time 306 andcalendar date 308 together may indicate the date and time of user 120being at place of habitation 108. As shown in FIG. 3, devices 110 maycommunicate (e.g., periodically) interaction 302 as a function of clocktime 306 and calendar date 308 to server 104, where the aforementionedcommunicated information may be analyzed and data associated with thebehavioral pattern(s) of user 120 generated.

FIG. 4 shows mobile device 102 and devices 110 communicating geospatiallocation 204, interaction 202 and interaction 302 through network 106 toserver 104. As shown in FIG. 4, server 104 may execute an algorithmmodule 440 through processor 172, algorithm module 440 includinginstructions to analyze geospatial location 204 and interaction 202 togenerate behavioral pattern data 402 of user 120. Based on interaction302, server 104 may also be configured to adjust behavioral pattern data402 (to be discussed below). In one or more embodiments, memory 174 mayhave instructions associated with algorithm module 440 stored therein.Algorithm module 440 may statistically analyze data (e.g., a record ofactivities of user 120 including geospatial location 204, interaction202 and interaction 302) from devices 110 and/or mobile device 102 togenerate behavioral pattern data 402. In one or more embodiments,behavioral pattern data 402 may predict future activities of user 120.

With regard to FIG. 4, algorithm module 440 may generate behavioralpattern data 402 based on geospatial location 204 and interaction 202 asa function of clock time 206 and calendar date 208. In one or moreembodiments, server 104 (e.g., through algorithm module 440) may adjustbehavioral pattern data 402 based on the periodic communication ofinteraction 302 as a function of clock time 306 and calendar date 308.

In one or more embodiments, server 104 may probabilistically predict anevent (e.g., event 470) associated with user 120 based on behavioralpattern data 402. In an example embodiment, algorithm module 440executing on server 104 may calculate a probability of a previous eventreoccurring in a temporal window of time. For example, event 470predicted may include an environmental control action 502A (e.g., alighting adjustment, a temperature adjustment, a setting adjustment, andan accessibility adjustment shown in FIG. 5) and/or a state changeaction 502B (a device power setting, a power activation procedure, apower discharge procedure, a schedule notification, an alertnotification, an arrival notification, a departure notification, a stateadjustment, and a motion adjustment shown in FIG. 5).

FIG. 5 shows event 470 being communicated to devices 110 through network106 as the result of analysis through algorithm module 440 based onbehavioral pattern data 402. The predicted temporal window of timeduring which event 470 is likely to occur may be related to the periodiccommunication of geospatial location 204 and interaction 202 asfunction(s) of clock time 206 and calendar date 208. In one or moreembodiments, server 104 may communicate event 470 as a recommendedaction required on devices 110 on behalf of user 120. In an examplescenario, user 120 may utilize a conference room (an example place ofhabitation 108) during 9:00 am to 10:00 am on every Monday to makepresentations on a projector device therein. Based on the couplingbetween the projector device and server 104, the projector device may beautomatically transitioned into an active mode of operation thereof(and/or adjustments performed thereon) during 9:00 am-10:00 am everyMonday. Additionally, user 120 may prefer a particular temperaturesetting on a thermostat in the conference room. Server 104 may onceagain automatically adjust the temperature setting to a preferredtemperature of user 120.

FIG. 6 shows a number of events 470 _(1-M) predicted at respectivetemporal windows of time (e.g., 470 ₁ at t₁ 610 ₁, 470 ₂ at t₂ 610 ₂ to470 _(M) at t_(M) 610 _(M)). It is obvious that the aforementionedtemporal windows of time are in a temporal future with respect toanalysis (e.g., clock time 206 of mobile device 102) being performedthrough algorithm module 440. Events 470 _(1-M) may be future activitiesand/or events predicted to occur in the aforementioned temporal windowsof time. For example, a temporal window of time may be 10 minutes aftera current time and may last for 15 minutes, 1 hour or 1 day. Anytemporal window of time in a temporal future with respect to analysisthrough algorithm module 440 is within the scope of the exemplaryembodiments discussed herein.

Although exemplary embodiments discussed above utilize server 104 topredict events 470 _(1-M), one or more of the aforementioned events 470_(1-M) may also be predicted at mobile device 102 (e.g., throughprocessor 210 executing instructions associated with an analogousalgorithm module 440 stored in memory 212). FIG. 7 shows mobile device102 predicting an event (e.g., event 702 analogous to events 470 _(1-M))instead of server 104. FIG. 8 shows an example scenario of mobile device102 predicting event 702. Here, device 110 may be mobile device 110Eassociated with a family member/friend/acquaintance of user 120 (e.g.,user 802). User 120 may be required to drive a car from a place ofresidence thereof to a place of residence of user 802 for a 2 pm meetingwith user 802. User 120 may be late for the meeting due to unforeseenevents. By analysis of geospatial coordinates (e.g., geospatial location204; here, user 120 may still be at the place of residence thereof, or,somewhere nearby) thereof through processor 210, mobile device 102 mayautomatically transmit event 702 to mobile device 110E indicating thatuser 120 would be late for the meeting (an example status information)on behalf of user 120.

Further, mobile device 102 may calculate an approximate time of arrivalat the place of residence (an example place of habitation 108) of user802 and automatically transmit the same to mobile device 110E. Based onperiodic analysis of geospatial location 204, mobile device 120 mayautomatically transmit updated expected time(s) of arrival. It isobvious that the aforementioned analysis may be performed at server 104instead of mobile device 210 as discussed with regard to other figures.Further, all other example scenarios involving analysis through mobiledevice 210 and/or server 104 are within the scope of the exemplaryembodiments discussed herein.

Additionally, it should be noted that while algorithm module 440 isshown as executing a set of instructions associated with analysis andprediction in the figures, algorithm module 440 may execute a singlealgorithm or a number of algorithms involved in the aforementionedanalysis or prediction. Further, server 104 may be a single server or anumber of servers networked together to function in an appropriatemanner. Still further, server 104 may also be interpreted to includecloud-based virtual computing platforms. Also, server 104 may time-stampand date-stamp geospatial location 204 of user 120 instead of mobiledevice 102.

In the example embodiments of FIG. 8 and relevant figures includingmobile device 102, mobile device 102 may also prompt user 102 to collectdata associated therewith and/or analyze the aforementioned data forprediction purposes. For example, user 102 may be given a capability topostpone, disable and/or skip analysis and prediction through mobiledevice 102 and/or server 104. In an example embodiment where theaforementioned analysis and prediction is offered as a service (hereserver 104 may be involved in the analysis and prediction), user 102 maybe monitored during the period of subscription. He/she may be requiredto skip the analysis/prediction through directly communicating with theservice provider therefor.

FIG. 9 shows a process flow diagram detailing the operations involved ina method of predicting an event associated with mobile device 102 and/ordevices 110, according to one or more embodiments. In one or moreembodiments, operation 902 may monitoring geospatial location 204 ofuser 120 of mobile device 102 having processor 210 communicativelycoupled to memory 212 through mobile device 102. In one or moreembodiments, operation 904 may involve date stamping and time stampinggeospatial location 204 of user 102 through mobile device 102. In one ormore embodiments, operation 906 may involve monitoring, through server104 having another processor 172 communicatively coupled to anothermemory 174 and/or mobile device 102, an interaction (e.g., interaction202, interaction 302) of user 120 with mobile device 102 and/or device110 communicatively coupled to server 104 based on geospatial locationof the user 204.

In one or more embodiments, operation 908 may involve predicting,through server 104 and/or mobile device 102, event 470 related to mobiledevice 102 and/or device 110 based on the monitoring of the interactionof user 120 therewith. In one or more embodiments, event 470 may be in atemporal future relative to the prediction. In one or more embodiments,operation 910 may involve enabling, through server 104 and/or mobiledevice 102, automatic performance of an action on mobile device 102and/or device 110 on behalf of user 120 in accordance with theprediction of event 470.

Although the present embodiments have been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the various embodiments.For example, the various devices, modules, analyzers, generators, etc.described herein may be enabled and operated using hardware circuitry(e.g., CMOS based logic circuitry), firmware, software (e.g., embodiedin a machine readable medium) etc.

In addition, it will be appreciated that the various operations,processes, and methods disclosed herein may be embodied in amachine-readable medium and/or a machine accessible medium compatiblewith a data processing system (e.g., a computer system), and may beperformed in any order (e.g., including using means for achieving thevarious operations). Accordingly, the specification and drawings are tobe regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: monitoring a geospatiallocation of a user of a mobile device having a processor communicativelycoupled to a memory through the mobile device; date stamping and timestamping the geospatial location of the user through the mobile device;monitoring, through at least one of a server having another processorcommunicatively coupled to another memory and the mobile device, aninteraction of the user with at least one of the mobile device and adevice communicatively coupled to the server based on the geospatiallocation of the user; predicting, through the at least one of the serverand the mobile device, an event related to the at least one of themobile device and the device based on the monitoring of the interactionof the user therewith, the event being in a temporal future relative tothe prediction; and enabling, through the at least one of the server andthe mobile device, automatic performance of an action on the at leastone of the mobile device and the device on behalf of the user inaccordance with the prediction of the event.
 2. The method of claim 1,wherein one of: the user is at a place of location of the devicecommunicatively coupled to the server, and the device is another mobiledevice associated with another user.
 3. The method of claim 2, furthercomprising: transmitting, through the device communicatively coupled tothe server, an interaction of the user with the device.
 4. The method ofclaim 1, comprising: analyzing, through the at least one of the serverand the mobile device, data associated with the monitoring to generate abehavioral pattern data of the user; and probabilistically predictingthe event based on the behavioral pattern data generated.
 5. The methodof claim 1, comprising: periodically transmitting the time-stamped andthe date-stamped geospatial location of the user to the server.
 6. Themethod of claim 1, further comprising adjusting data associated with thepredicted event based on periodic monitoring of the time-stamped and thedate-stamped geospatial location of the user through the at least one ofthe server and the mobile device.
 7. The method of claim 2, comprisingtransmitting status information of the user of the mobile device to theanother mobile device as the automatic action performed on the mobiledevice on behalf of the user.
 8. A system comprising; a computernetwork; a mobile device communicatively coupled to the computernetwork, the mobile device comprising a processor communicativelycoupled to a memory; and another device communicatively coupled to thecomputer network, wherein the processor of the mobile device isconfigured to execute instructions for: receiving a geospatial locationof a user of the mobile device, date stamping and time stamping thegeospatial location of the user, monitoring an interaction of the userwith at least one of the mobile device and the another devicecommunicatively coupled to the computer network based on the geospatiallocation of the user, predicting an event related to the at least one ofthe mobile device and the another device based on the monitoring of theinteraction of the user therewith, the event being in a temporal futurerelative to the prediction, and enabling automatic performance of anaction on the at least one of the mobile device and the another deviceon behalf of the user in accordance with the prediction of the event. 9.The system of claim 8, wherein one of: the user is at a place oflocation of the another device communicatively coupled to the computernetwork, and the another device is another mobile device associated withanother user.
 10. The system of claim 9, wherein the another device isconfigured to transmit an interaction of the user therewith to themobile device to enable analysis thereof through the mobile device. 11.The system of claim 8, wherein the processor of the mobile device isfurther configured to execute instructions for: analyzing dataassociated with the monitoring to generate a behavioral pattern data ofthe user, and probabilistically predicting the event based on thebehavioral pattern data generated.
 12. The system of claim 8, whereinthe processor of the mobile device is further configured to executeinstructions for adjusting data associated with the predicted eventbased on periodic reception of the geospatial location of the user. 13.The system of claim 9, wherein the processor of the mobile device isconfigured to execute instructors for enabling transmission of statusinformation of the user thereof to the another mobile device as theautomatic action performed on the mobile device on behalf of the user.14. A system comprising; a computer network; a mobile devicecommunicatively coupled to the computer network; another devicecommunicatively coupled to the computer network; and a servercommunicatively coupled to the mobile device and the another devicethrough the computer network, the server comprising a processorcommunicatively coupled to a memory, and the processor being configuredto execute instructions for: receiving a date-stamped and time-stampedgeospatial location of a user of the mobile device, monitoring aninteraction of the user with at least one of the mobile device and theanother device communicatively coupled to the computer network based onthe geospatial location of the user, predicting an event related to theat least one of the mobile device and the another device based on themonitoring of the interaction of the user therewith, the event being ina temporal future relative to the prediction, and enabling automaticperformance of an action on the at least one of the mobile device andthe another device on behalf of the user in accordance with theprediction of the event.
 15. The system of claim 14, wherein one of: theuser is at a place of location of the another device communicativelycoupled to the computer network, and the another device is anothermobile device associated with another user.
 16. The system of claim 15,wherein the another device is configured to transmit an interaction ofthe user therewith to the server to enable analysis thereoftherethrough.
 17. The system of claim 15, wherein the processor of theserver is further configured to execute instructions for: analyzing dataassociated with the monitoring to generate a behavioral pattern data ofthe user, and probabilistically predicting the event based on thebehavioral pattern data generated.
 18. The system of claim 15, whereinthe processor of the server is further configured to executeinstructions for adjusting data associated with the predicted eventbased on periodic reception of the geospatial location of the user. 19.The system of claim 16, wherein the processor of the server isconfigured to execute instructions for enabling transmission of statusinformation of the user of the mobile device to the another mobiledevice as the automatic action performed on the mobile device on behalfof the user.
 20. The system of claim 16, wherein the date stamping andthe time stamping of the geospatial location of the user is performedone of: through the server and through the mobile device.